For researchers, data professionals and AI teams

Statistics Coaching for Data, Research and AI Teams

Two averages differ by three points. Is that a real difference or just noise? Whether you are writing a thesis, reporting to a board or comparing two machine learning models, the question is the same. We coach you in the statistics behind your results, from sampling and uncertainty to significance testing and honest comparison, using your own data so every idea applies to work you do.

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Best forStudents, researchers, analysts and AI teams

FormatIndividual or small group sessions

CoversSampling, testing, regression, evaluation

ToolsSPSS, R, Python, Excel

The short answer

What is statistics coaching?

Statistics coaching is practical, hands on training in the statistical thinking behind research and data work. We use your own datasets, analyses and results, so every concept lands on a problem you actually have, whether that is a dissertation chapter, a monthly report or a model comparison.

Most people who work with data are strong in their own field. The gaps tend to be in sampling, uncertainty, significance and study design. Those gaps are where a result looks convincing but does not hold up under review, in a journal, a board meeting or production.

Where statistics enters

Where coaching fits in your model development cycle

Most AI and machine learning teams follow the same cycle, and the statistics matter most at evaluation. We join where a claim about a model has to be tested, then help the team act on the results. Smaller teams often combine these roles.

Their work

Our part

1. Problem

ML engineer or product lead

Chooses the task, the users and the success metric.

Our partHelps turn a vague goal into a measurable claim to test.

2. Experiment design

Data scientist

Plans data splits, baselines and the number of runs.

Our partChecks the design can detect the difference you care about.

3. Training

ML engineer

Trains and tunes models, and logs every run.

Our partReviews logs for leakage and unfair comparisons between models.

4. Evaluation

Data scientist

Compares models on test data and reports the scores.

Our partAdds confidence intervals and significance tests, so small gains are not mistaken for real ones.

5. Release decision

Product lead and tech lead

Decides whether the new model goes live.

Our partExplains the uncertainty behind the decision in plain language.

6. Monitoring

MLOps and operations team

Tracks accuracy and drift in live use.

Our partDesigns the checks that show when a model has truly changed.

Who it is for

Built for teams like yours.

Researchers and students

Methods support for theses, papers and grant applications, with every step explained so you can defend your analysis.

Data analysts and professionals

The statistical reasoning behind your reports: sampling, uncertainty, significance and choosing the right test for the question.

Business and product teams

Plan A/B tests with enough power, avoid peeking at results early and read the size of an effect correctly.

AI and ML teams

Compare models with confidence intervals, design evaluation sets for LLM and RAG systems and check whether human raters agree.

What you get

Clear deliverables, agreed up front.

Every engagement is shaped around your work, with notes and code you keep. Every project is quoted as a fixed price after a free assessment, so you know the cost before work begins.

A short skills review of your current analysis or evaluation practice
Sessions built on your own data, models or reports
Reusable templates in Python, R or SPSS syntax
A statistical review of one analysis, test or model comparison
Plain English notes your team or supervisor can reuse
Follow up support by email between sessions

Packages and pricing

Pick the depth you need.

Starting prices are shown for each option. Your final price is a fixed quote, agreed after a free assessment of your data and scope, so there are no surprises.

One question, answered live

Single Session

A focused call on one method, model result or analysis decision.

From $60per session

60 to 90 minute live session
Review of your output or code beforehand
Written summary with next steps

Get a fixed quote

Recommended for most learners

Skills Pack

A structured series that builds one skill properly, at your pace.

From $275pack of 5

Five sessions on an agreed topic
Exercises using your own data
Email questions between sessions
Reusable notes and code

Get a fixed quote

For AI and research teams

Team Review

A statistical audit of an experiment, evaluation or paper before you rely on it.

From $600fixed project

Review of design, metrics and tests
Confidence intervals and power check
Written findings with fixes
Debrief call with your team

Get a fixed quote

How it works

Four steps from question to answer.

Step 1
Discovery call
We review what you are working on and where results feel uncertain.
Step 2
Learning plan
You receive a short plan of topics mapped to your real work.
Step 3
Coaching sessions
Live sessions on your own data, with exercises between them.
Step 4
Review and handover
We review one real analysis together and leave you templates to repeat it.

Questions we help you answer

If you are asking these, we can help.

Sampling and sample sizeConfidence intervals and bootstrappingHypothesis testing and p valuesStatistical powerRegression and causal thinkingA/B testing without peekingPrecision, recall and calibration for modelsRetrieval metrics for RAG (precision at k, recall at k, MRR)Rater agreement (Cohen’s kappa)Bias, variance and overfitting

Tools we use

SPSSRPythonJupyterExcelStata

?Is this difference real or just chance?
?How big should my sample be?
?Which test fits my data and design?
?Is Model B really better than Model A?
?How long should this A/B test run?
?Can I trust my survey scale or my human labels?

FAQ

Questions about Statistics Coaching

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Do I need a statistics background to join?

No. Sessions start from where you are. Students, analysts and engineers with no formal statistics training get as much from them as experienced practitioners, because we work on your own examples.

Can you help with an analysis I have already started?

Yes. We review your current analysis, explain what is correct and what needs changing, and show you how to check it yourself.

Is this a course or consulting?

It sits between the two. You learn the concepts, and we apply them to your live work, so you leave with skills as well as results.

Can individuals book, or only teams?

Both. Individuals can book one on one sessions, and teams can book group sessions tailored to their work.

Can you coach us on evaluating AI models and RAG systems?

Yes. We cover evaluation set design, retrieval metrics, rater agreement and confidence intervals for any score you report.

Will you do our assignments or exams?

No. For students we provide coaching and explanation only, in line with academic integrity rules.

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